AGGRESSIVE MOVEMENT FEATURE DETECTION USING COLOR-BASED APPROACH ON THERMAL IMAGES

Abstract

Thermal imaging technology can be used to detect aggressive levels in humans based on the radiated heat from their face and body. Previous researches have proposed an approach to figure out human aggressive movements using Horn-Schunck optical flow algorithm in order to find the flow vector for all video frames but still not strong enough to confirm and verify the existence of an aggressive movement. In this work, we propose an approach by using thermal videos for frontal views of the human body which is face view. Then, video frames are collected using thermal camera and further extracted into thermal images. We use thermal imaging to monitor the face including prefrontal and periorbital region’s thermal variations and test whether it can offer a discriminative signature for detecting aggressiveness. We start by presenting an overview of 3400 thermal images extracted from 50 participants. The results obtained is promising where aggressive and non-aggressive features can be detected by using color-based approach.